Back
Remote mid-level generalist data role with common AWS/BI stack and broad hiring pool increases competition.
Core data engineering skills transfer easily across industries despite fintech context preference.
Explicit 3–4 year requirement plus mandatory AWS, Redshift, Airflow, and BI skills create high shortlisting strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own end-to-end delivery and maintenance of dashboards in QuickSight and ThoughtSpot, including new metrics, filters, SPICE refresh, and validation.
Build and maintain batch and streaming ETL pipelines on AWS using Glue (PySpark), S3, Redshift, and Airflow DAGs, including near-real-time ingestion.
Manage data quality, root cause analytics discrepancies, monitor pipeline health with CloudWatch, and participate in incident triage and RCA.
3-4 years of experience in data engineering and/or analytics engineering.
Strong SQL skills on Redshift or similar MPP warehouse; Python and PySpark proficiency.
Hands-on experience with AWS data stack: S3, Glue, Redshift, CloudWatch, and workflow orchestration using Apache Airflow.
Experience with BI dashboard tools such as QuickSight, ThoughtSpot, Tableau, or Power BI.
Operates effectively in cross-functional teams including product, engineering, DevOps, and customer support, showing strong collaboration and communication skills.
Quickly grasps unfamiliar product domains and data models to translate product changes into dashboard and data model updates.
Experienced with AWS streaming tech (Kafka/MSK CDC), data modeling, and fintech or B2B SaaS analytics environments (preferred but not mandatory).